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Identification and inference in discrete choice models with imperfect information

Gualdani, Cristina and Sinha, Shruti (2019) Identification and inference in discrete choice models with imperfect information. TSE Working Paper, n. 19-1049, Toulouse

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Official URL: http://tse-fr.eu/pub/33017

Abstract

In this paper we study identification and inference of preference parameters in a single-agent, static, discrete choice model where the decision maker may face attentional limits precluding her to exhaustively process information about the payoffs of the available alternatives. By leveraging on the notion of one-player Bayesian Correlated Equilibrium in Bergemann and Morris (2016), we provide a tractable characterisation of the sharp identified set and discuss inference under minimal assumptions on the amount of information processed by the decision maker and under no assumptions on
the rule with which the decision maker resolves ties. Simulations reveal that the obtained bounds on the preference parameters can be tight in several settings of empirical interest.

Item Type: Monograph (Working Paper)
Language: English
Date: November 2019
Place of Publication: Toulouse
Uncontrolled Keywords: Discrete choice model, Bayesian Persuasion, Bayesian Correlated Equilibrium, Incomplete Information, Partial Identification, Moment Inequalities.
Subjects: B- ECONOMIE ET FINANCE
Divisions: TSE-R (Toulouse)
Institution: Université Toulouse 1 Capitole
Site: UT1
Date Deposited: 18 Nov 2019 09:29
Last Modified: 06 Dec 2019 10:10
["eprint_fieldname_oai_identifier" not defined]: oai:tse-fr.eu:33017
URI: http://publications.ut-capitole.fr/id/eprint/32906

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